Autonomous vehicles are used for applications in hazardous environments such as nuclear reactors, or in carrying out tedious tasks such as transportation and floor cleaning. Transport applications require point-to-point or path following navigation while avoiding obstacles. Floor cleaning applications require nearly complete coverage of the floor, with enough overlap not to leave gaps, but with a minimum of repeated visits to the same area to avoid inefficiency.
A variety of techniques are used for navigation of autonomous vehicles. They generally use odometry to compute and measure vehicle position, coupled with sensors which inform the navigation system of the positions of obstacles or landmarks. Such systems often involve preprogramming the vehicle with information about the geometric layout of the environment, and the positions and characteristics of key landmarks. Daemmer (U.S. Pat. No. 4,500,970) teaches a system which registers vehicle position with the preprogrammed checkpoints in the environment using a variety of sensors. Maddox et al (U.S. Pat. No. 4,710,020) teaches a system in which the vehicle registers its position visually with respect to an active artificial beacon. Kaneko et al (U.S. Pat. No. 4,558,215) describe a system for detecting obstacles by projecting a light beam and using the geometry of reflection for range and position measurement. Krishnamurthy et al ("Helpmate: A Mobile Robot for Transport Applications", Proceedings of SPIE Conference on Mobile Robots, Cambridge, Mass., Nov. 11, 1988) describes a multisensor navigation system in which the robot is trained to a particular environment. Moravec et al ("High Resolution Maps from Wide Angle Sonar", IEEE, 1985) describes a robot navigation system based on sonar.
The prior art techniques are too complex, expensive, and/or large in size to be efficient in low cost applications, such as floor vacuuming in private homes, offices or hotel rooms. In these applications, simpler equipment and lower cost are essential, while preserving efficient performance. Also, whereas in transport systems, the object is to avoid proximity to obstacles, in floor cleaning applications, the object is to come as close as possible to obstacles to assure complete floor coverage. In large scale cleaning operations, it is customary to use cleaning patterns which cover the floor in regular, repetitive patterns such as parallel back and forth patterns used in plowing fields, or spiralling patterns. These are efficient when the scale of the open floor space greatly exceeds the scale of the vehicle, such as in warehouses, dining halls, store aisles, and the like. Such methods are most efficient when some prior knowledge of the layout of the environment is preprogrammed into the autonomous vehicle. However, in small cluttered areas such as hotel rooms, offices, and rooms in private home, such regular patterns cannot be achieved within the confines of the environment, and there is too much variation in position of furniture and obstacles to use a preprogrammed map.
Power requirements for vacuuming are considerably higher than for vehicle transportation. Batteries to provide self-contained power sources are heavy, reducing the efficiency of payload capacity. Economies of scale favor batteries in large equipment, but not in small scale equipment of the scale of domestic vacuum cleaners. For these applications a cord plugged into household line current, for example 120 Volts in the U.S., is preferable. However, one would expect that for autonomous operation, this mechanical connection and trailing cord interfere with the free motion of the vehicle.